Lung cancer is one of the leading causes of death from cancer, with a current mortality rate of approximately 160,000 deaths per year in the United States. Annual Computed Tomography (CT) screening can result in an approximately 20% reduction in lung-cancer mortality rates for high-risk patients. As such, widespread screening of high-risk lung cancer patients has been generally implemented in medical practice. Though supportive of mortality reduction, a substantial majority of suspicious nodules in the National Lung Screening Trial (NLST) turn out to be negative (e.g., approximately 96%) and screening this population can result in up to approximately $12B of unnecessary procedures, including biopsies, surgeries, and imaging studies on negative patients.
Current approaches to improve diagnostic sensitivity and specificity typically improve one aspect at the expense of the other. For example, using the Lung Imaging Reporting and Data System (Lung-RADS) diagnostic criterion with the NLST dataset, the false positive rate can be reduced. However, Lung-RADS guidelines also reduce the rate of detection (sensitivity). Considering the low survival rate of late stage lung cancer, decreasing the false positive rate alone is not sufficient and early detection is important. Accordingly, a need exists for improved analysis information for diagnosis and treatment of patients.